Multimodal access of meeting recordings

ABSTRACT

A meeting recorder captures multimodal information of a meeting. Subsequent analysis of the information produces scores indicative of visually and aurally significant events that can help identify significant segments of the meeting recording. Textual analysis can enhance searching for significant meeting segments and otherwise enhance the presentation of the meeting segments.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is related to the following commonly owned andco-pending U.S. patent applications which are hereby incorporated byreference for all purposes:

-   -   U.S. patent application Ser. No. 09/52,1252 titled “Method and        System for Information Management to Facilitate the Exchange of        Ideas During a Collaborative Effort,” filed Mar. 8, 2000;    -   U.S. patent application Ser. No. 10/081,129 titled “Multimedia        Visualization & Integration Environment,” filed Feb. 21, 2002;        and    -   U.S. patent application Ser. No. 10/174,522 titled        “Television-based Visualization and Navigation Interface,” filed        Jun. 17, 2002.

BACKGROUND OF THE INVENTION

The present invention relates generally to multimedia meeting recordingsand more specifically to access of multimedia meeting recordings.

Progress in the business world typically finds its beginnings with aseries of meetings. Meetings are usually drawn out unstructured affairs,consisting of mostly irrelevant information. However, there usually areone or two defining moments that occur during a meeting which can propelthe enterprise in the forward direction toward success, or if missed,can result in yet another failed business venture.

Many businesspersons visit remote locations and participate in meetingswith different people on a regular basis. A common task that must beperformed at some time after the meeting is the creation of a summary ofwhat happened during the meeting. The summary may include reports of whosaid what, the ideas that were conceived, the events that occurred, andthe conclusions that were reached. Oftentimes, it is not just thespecific conclusions but also the reasons they were reached and thepoints of view expressed by the meeting participants that are important.

Producing an accurate meeting summary is a time-consuming anderror-prone process, especially if the only record available is one'sown memory, perhaps supplemented with hastily handwritten notes. Acommonly used portable memory aid is the audiocassette recorder. It canbe effective, but lacks the ability to capture important events thatcould be helpful later, such as gestures, images of participants, bodylanguage, drawings, and so on. An easy-to-use method for incorporatingvideo data would help solve this problem.

Meeting recordings can help. Capturing the content of meetings is usefulin many respects. Recordings of a meeting can capture the meetingactivity and then later be reviewed as needed to refresh one's memory.Knowing that a meeting is being recorded allows the participants to moreeffectively concentrate on the meeting discussion since the details canbe later reviewed in an offline manner. Audio-visual recordings ofmeetings provide the capability for reviewing and sharing meetings,clarifying miscommunications, and thus increase efficiency.

Recognizing this need, several meeting recorder systems have beendeveloped in recent years. A multimodal approach to creating meetingrecords based on speech recognition, face detection and people trackinghas been reported in CMU's Meeting Room System described by Foote, J.and Kimber, D., FlyCam: Practical Panoramic Video and Automatic CameraControl, Proceedings of International Conference on Multimedia & Expo,vol. 3, pp. 1419-1422, 2000. Gross, R., Bett, M. Yu, H., Zhu, X., Pan,Y., Yang, J., Waibel, A., Towards a Multimodal Meeting Record,Proceedings of International Conference on Multimedia and Expo, pp.1593-1596, New York, 2000 also describe a meeting recorder system.

However, people generally prefer not want to watch a recorded meetingfrom beginning to end. Like the meeting from which the recording wasproduced, recorded meetings are not amenable to a hit-or-miss searchstrategy. After fast-forwarding a few times in a meeting video whilelooking for something, most people will give up unless what they areseeking is important enough to suffer the tedium.

More often than not, people are only interested in an overview of themeeting or just the interesting parts. Enabling efficient access tocaptured meeting recordings is essential in order to benefit from thiscontent. Searching and browsing audiovisual information can be a timeconsuming task. Two common approaches for overcoming this probleminclude key-frame based representations and summarization using videoskims. Key-frame based representations have proven to be very useful forvideo browsing as they give a quick overview to the multimedia content.A key-frame based technique is described by S. Uchihashi, J. Foote, A.Girgensohn, and J. Boreczky, Video Manga: Generating SemanticallyMeaningful Video Summaries, ACM Multimedia, (Orlando, Fla.) ACM Press,pp. 383-392, 1999.

On the other hand, video skims are content-rich summaries that containboth audio and video. Efficiently constructed video skims can be usedlike movie trailers to communicate the essential content of a videosequence. For example, A. Waibel, M. Bett, et al., Advances in AutomaticMeeting Record Creation and Access, Proceedings of ICASSP, 2001 proposesummarization of meeting content using video skims with auser-determined length. The skims are generated based on relevanceranking and topic segmentation using speech transcripts.

A summarization technique for educational videos based on shot boundarydetection, followed by word frequency analysis of speech transcripts, issuggested by C. Taskiran, A. Amir, D. Ponceleon, and E. J. Delp,Automated Video Summarization Using Speech Transcripts, SPIE Conf. on Stand Ret. for Media Databases, pp. 371-382, 2002.

A method for summarizing audio-video presentations using slidetransitions and/or pitch activity is presented by He, L., Sanocki, E.,Gupta, A., and Grudin, J., Auto-summarization of audio-videopresentations, In Proc. ACM Multimedia, 1999. The authors suggest amethod of producing presentation summaries using video channel, audiochannel, speaker's time spent on a slide, and end user's actions. Theauthors discuss the use of pitch information from the audio channel, buttheir studies showed the technique as being not very useful for summarypurposes. Instead, they indicate that the timing of slide transitionscan be used to produce the most useful summaries.

Motion content in video can be used for efficiently searching andbrowsing particular events in a video sequence. This is described, forexample, by Pingali, G. S., Opalach, A., Carlbom, I., MultimediaRetrieval Through Spatio-temporal Activity Maps, ACM Multimedia, pp.129-136, 2001 and by Divakaran, A., Vetro, A., Asai, K., Nishikawa, H.,Video Browsing System Based on Compressed Domain Feature Extraction,IEEE Transactions on Consumer Electronics, vol. 46, pp. 637-644, 2000.

As a part of the Informedia™ project, Christel, M., Smith, M., Taylor,C. R., and Winkler, D. Evolving Video Skims into Useful MultimediaAbstractions, Proc. of the ACM CHI, pp. 171-178, 1998 compared videoskimming techniques using (1) audio analysis based on audio amplitudeand term frequency-inverse document frequency (TF-IDF) analysis, (2)audio analysis combined with image analysis based on face/text detectionand camera motion, and (3) uniform sampling of video sequences. Theyreported that audio analysis combined with visual analysis yieldsignificantly better results than skims obtained purely by audioanalysis and uniform sampling.

In Sun, X., Foote, J., Kimber, D., and Manjunath, Panoramic VideoCapturing and Compressed Domain Virtual Camera Control, ACM Multimedia,pp. 229-238, 2001, a user-oriented view is provided based on speakermotion. A perhaps more intuitive solution is to compute the speakerdirection as suggested by Rui, Y., Gupta, A., and Cadiz, J., ViewingMeetings Captured by an Omni-directional Camera, ACM CHI 2001, pp.450-457, Seattle, Mar. 31-Apr. 4, 2001. Techniques such as summarizationand dialog analysis aimed at providing a higher level of understandingof the meetings to facilitate searching and retrieval have been exploredby Hauptmann, A. G., and Smith, M., Text Speech and Vision for VideoSegmentation: The Informedia Project, Proceedings of the AAAI FallSymposium on Computational Models for Integrating Language and Vision,1995.

Analysis of the audio signal is useful in finding segments of recordingscontaining speaker transitions, emotional arguments, and topic changes,etc. For example, in S. Dagtas, M. Abdel-Mottaleb, Extraction of TVHighlights using Multimedia Features, Proc. of MMSP, pp. 91-96, 2001,the important segments of a sports video were determined based on audiomagnitude.

In a paper by Bagga, J. Hu, J. Zhong and G. Ramesh, Multi-sourceCombined-Media Video Tracking for Summarization, The 18th InternationalConference in Pattern Recognition (ICPR'02) Quebec City, Canada, August2002, the authors discuss the use of text analysis (from closedcaptioning data) and video analysis to summarize video sequences. Textanalysis is performed to find topics by using a similarity measure basedon Salton's Vector Space Model. Visual analysis is based on dominantcolor analysis. Feature vectors found by text and visual analysis arenormalized, combined into one feature vector, and hierarchicalclustering is used for final clustering. Clustering is performed acrossmultiple videos to find the common news stories. These stories are thenused in the summary. Their technique identifies similarities acrossdifferent video tracks.

Speech content and natural language analysis techniques are commonlyused for meeting summarization. However, language analysis-basedabstraction techniques may not be sufficient to capture significantvisual and audio events in a meeting, such as a person entering the roomto join the meeting or an emotional charged discussion. Therefore, itcan be appreciated that continued improvement in the area of processingmeeting recordings is needed to further facilitate effective access andretrieval of meeting recording information.

SUMMARY OF THE INVENTION

A method for creating video skims is based on audio and visual activityanalysis together with text analysis. In an illustrative embodiment ofthe invention, detection of important visual events in a meetingrecording can be achieved by analyzing the localized luminancevariations in consideration with the omni-directional property of thevideo captured by our meeting recording system. In another illustrativeembodiment, audio activity analysis is performed by analyzing sounddirections—indicating different speakers—and audio amplitude. A furtheraspect of the present invention is incorporation of text analysis basedon the Term Frequency—Inverse Document Frequency measure. The resultingvideo skims can capture important segments more effectively as comparedto the skims obtained by uniform sampling. It can be appreciated thatthe techniques according to the present invention can be applied to anymultimedia recording, wherein meeting recordings constitute only asubset of the broader category of multimedia recordings.

BRIEF DESCRIPTION OF THE DRAWINGS

An understanding of the present invention is provided by the followingfigures along with the discussion that follows, where:

FIG. 1 shows a high level generalized block diagram of a meetingrecording and access system in accordance with an illustrativeembodiment of the present invention;

FIG. 2A represents an original frame of video as captured by a videorecording device;

FIG. 2B represents a video frame reconstructed from only the DCcoefficients obtained from an MPEG encoding of the video frame shown inFIG. 2A;

FIG. 3 shows an enlarged view of the doughnut-shaped video imagescaptured using an omni-directional video recorder;

FIG. 4 illustrates an example of video significance metrics according tothe present invention;

FIG. 5 illustrates an example of audio significance metrics according tothe present invention;

FIG. 6 is a high level diagram generally illustrating the process ofidentifying significant meeting events;

FIG. 7 illustrates transcript processing according to an aspect of thepresent invention;

FIG. 8A illustrates a dewarped image;

FIG. 8B shows the results of a participant identification process;

FIG. 9 illustrates best shot processing of an image;

FIGS. 9A and 9B show schematic representations of criteria that can beused for best shot processing;

FIG. 10 shows an illustrative embodiment of a graphical user interface(GUI) in accordance with the invention;

FIG. 11 shows an enlarged view of a portion of the timeline shown inFIG. 10, illustrating speaker transitions along the timeline;

FIG. 12 shows a version of the GUI of FIG. 10, highlighting the speakertransition channels;

FIG. 13 shows an enlarged view of a portion of the timeline shown inFIG. 10, illustrating visual activity metrics along the timeline;

FIG. 14 shows a version of the GUI of FIG. 10, highlighting the visualactivity channel;

FIG. 15 illustrates a example of a meeting summary display; and

FIG. 16 illustrate a meeting summary display generated using SMIL.

DESCRIPTION OF THE SPECIFIC EMBODIMENTS

From the following it will be appreciated that the present invention hasbroad application generally to multimedia information. However, tofacilitate a discussion of the invention sufficient to enable itspractice, the present invention will be described in the context of ameeting recording system. As will become apparent, a meeting recordingsystem can prove to be a rich source of various modes of information andthus serves as an appropriate example for describing the numerousaspects of the present invention. However, it is nonetheless noted thatmany aspects of the present invention are generally applicable to thebroader category of “multimedia content.”

The meeting recording and access system exemplar shown in FIG. 1comprise a meeting recorder component 102 for capturing a variety ofmedia information that can be produced during the activity of a meeting.For example, the meeting recorder can comprise a video recording system104 a to produce a visual recording of the meeting participants andpossibly any other visual activity that may be present such as a videopresentation made during the meeting. In one embodiment of theinvention, for example, the video recording system can be anomni-directional camera having a parabolic mirror for capturing apanoramic view of the meeting room. Such cameras are known. The videostream comprises doughnut-like images.

The meeting recorder 102 may further comprise an audio recording system104 b to produce an audio recording of the conversations of the meetingparticipants, including other sound sources such as a videopresentation, output from a speaker phone, etc., and in general caninclude any sound that might occur during the meeting (e.g., slamming ofa door, sound of a fire engine or an airplane passing by, etc.).Typically, the audio recording system includes a plurality ofmicrophones to pick up the different speakers, and as will be explained,to allow for locating sound sources.

In a particular embodiment of the invention, the audio recording systemcan also provide sound localization information. For example, sound datacan be collected with microphone arrays. Subsequent processing of thedata by known sound localization techniques, either in real-time oroff-line, can be employed to find the direction of the sound. In aparticular embodiment of the invention, sound localization is performedin real-time to avoid the need of handling and saving multiple channelsof audio data. This approach may be suitable in a compact configurationsuch as in a portable system, for example.

The audio signal is processed in segments of 25 msec. Since we areinterested only in human speech, segments that do not contain speech inat least one of the sound channels can be ignored. Following speechdetection, 360-degree sound localization can be determined in thefollowing manner. For each pair of microphones that are diagonallysituated, an angle between 0 and 180 degrees is calculated based onphase difference. This angle defines a cone of confusion centered at themidpoint of the diagonal. In theory, the intersection of two conescomputed from both diagonal pairs defines the azimuth and elevation ofthe sound source. Unfortunately, the angle computed by each pair is notperfect. Moreover, phase difference measured on a finite sampling rateover a small baseline is discrete, and the angular resolution over alldirections is non-uniform. Higher resolution is obtained near thecenter, and is lower towards both ends. Therefore, we need to computethe intersection of two cones of unequal thickness, if they intersect atall. Furthermore, we want to take into consideration the confidenceassociated with each angle estimate.

To resolve these issues, we use an accumulator over the parameter spaceof azimuth by elevation. Azimuth varies from 0 to 360 degrees andelevation varies from 0 to 90 degrees. For each possible (azimuth,elevation) pair covered by each cone, its entry is incremented by theconfidence associated with the cone. The highest scoring entry in theaccumulator corresponds to the best parameter estimate. All entries inthe accumulator are decayed by a factor at the end of each segment.However, in trying to estimate both azimuth and elevation, we found thesolution unstable and sensitive to the quantization chosen. Furthermore,it does not account for the fact that sound sources close to the middleare detected more accurately than those close to either end. Therefore,the scores at all elevations are summed up for each azimuth, and thebest azimuth is returned if its score exceeds a threshold. Then, foreach segment where speech is found, a triplet of time-stamp, angle, andscore, denoted by (t, θ_(i), w_(i)), is written to a file. We observedthis process is capable of performing in real-time, consumingapproximately 25% to 40% CPU load on a 933 MHz PC.

Text capture devices 104 c can be incorporated into the meeting recorder102. For example, a scanner can be provided to allow scanning ofdocuments that are handed out during the meeting. Notes generated duringthe meeting can be scanned in. The text capture can include associatingsome form of information identifying the originator of the text beingcaptured.

As a catch-all, it can be appreciated that other capture devices 104 dcan be incorporated into the meeting recorder 102. For example, slidesfrom a slide presentation can be captured. Where a whiteboard is used,one can appreciate that the contents of the whiteboard can be capturedusing appropriate known whiteboard capture techniques. Meetingparticipants can use graphics tablets or similar input devices to recordhandwritten notes. Such devices can facilitate the capture of notes byobviating the step of scanning in notes written on conventional mediasuch as paper.

FIG. 1 shows the meeting recorder 102 and the various data capturecomponents 104 a-104 d as separate blocks. However it can be appreciatedfrom a system architecture point of view that the distribution of theimplementational block comprising these modules can vary greatly. Theimplementation details of a specific embodiment of the invention islikely to depend on system costs, performance criteria, and otherconsiderations not relevant to the practice of the present invention.For example, the meeting recorder can comprise an omni-directionalcamera that produces a conventional analog video signal. The videosignal can be provided to a separate video processor to produce asuitable digital format for storage and processing by a computer system.A common digital format can be used, such as the MPEG (motion pictureexperts group) format. However, it in understood that other formats suchas MPEG-1, MPEG-2, MPEG-4, H.263, H.263L, JVT/H.26L, and otherblock-based video compression formats can be used. Similarly, themeeting recorder 102 may simply incorporate one or more microphones forcapturing the audio produced in the meeting, outputting an analog audiosignal. The audio signal can then be provided to an audio processor tobe converted to a suitable digital signal The audio processor may or maynot be part of the meeting recorder. Additional processing can beperformed to generate sound localization information, which can occuroff-line using a separate computer system. In a given implementation,the audio processor and the video processor can be the same processor.

FIG. 1 shows that the information captured by the capture components 104a-104 d, collectively represented by the data line 103, can be stored insome sort of data store represented functionally as a storage component112 for subsequent processing and retrieval. It is noted that thiscomponent is only a logical representation of the actual storagehardware; as are the other components shown in the figure. Differentstorage devices may be used to store different data, including captureddata and processed data.

A metrics generator functionality 106 produces a variety of metrics fromthe various information collected in accordance with the invention(additional details about the computations provided below) and then canbe stored in the storage component 112. In one embodiment of theinvention, the information can feed directly to the metrics generatorfrom the various data capture device 104 a-104 d. In another embodiment,the source data for the metric generator can be obtained from thestorage component 112 in an off-line manner.

An access engine functionality 124 provides access to the informationcontained in the storage component. As will be explained below, thecaptured information can be selected in accordance with the inventionbased on various combinations of the computed metrics. An interfaceengine functionality 122 provides an appropriate user interface to theaccess engine functionality. For example, a graphical user interface(GUI) may be appropriate to facilitate browsing the recorded meetinginformation. It is noted here that a “user” need not be a conventionalhuman user. Another machine can serve as the “user” of the presentinvention. In such a case, the interface engine functionality can bedefined by an appropriate data exchange protocol. The interface enginefunctionality can simply be a suitable application programming interface(API), defining a library of utilities to facilitate implementing asuitable GUI, or a machine interface, or even a simple command lineinterface (CLI).

The discussion will now turn to various metrics that can be produced bythe metrics generator functionality 106 according to the presentinvention. Consider first the video information collected by the videocapture functionality 104 a. In accordance with an embodiment of thepresent invention, the video is captured with an omni-directional cameraand converted to the MPEG-2 data format for storage and subsequentaccess.

In a video of a typical meeting sequence, most of the time there isminimal motion. High motion segments of video usually correspond tosignificant events. For example, a participant getting up to make apresentation, someone joining or leaving the meeting, or just makinggestures, could be important for searching and recalling some segmentsof the meeting.

In accordance with the invention, a visual significance measure isgenerated based on local luminance changes in a video sequence. A largeluminance difference between two consecutive frames is generally anindication of a significant content change, such as a person getting upand moving around. However, insignificant events, such as dimming thelights or all the participants moving slightly, may also result in alarge luminance difference between two frames. In order to reduce thelikelihood of identifying such events as being significant, the visualsignificance measure, according to an embodiment of the invention, canbe determined by considering luminance changes occurring in smallwindows of a video frame rather than a single luminance change of thewhole frame.

The luminance changes can be computed by computing the luminancedifference between the consecutive intra coded (I) frames in the MPEG-2compressed domain. We employ I-frames because the luminance values inI-frames are coded without prediction from the other frames, and theyare therefore independently decodable. We compute luminance differenceson the average values of 8×8 pixel blocks obtained from the DCcoefficients. The DC coefficients are extracted from the MPEG bit streamwithout full decompression. Average values of the 8×8 pixel blocks arefound by compensating for the DC prediction and scaling. In a particularimplementation, the DC coefficients are obtained from the bit streamusing a modified version of the publicly available TMN MPEG-2 decoderavailable at the FTP site:

-   -   ftp://mm-tp.cs.berkeley.edu/pub/multimedia/mpeg2/software/).

We modified the decoder to only parse the Intra coded frames and to skipsome of the decoding operations (e.g., Inverse DCT, Inversequantization, motion compensation). Because full decoding andreconstruction is not performed, a one hour video can be processed inless than 4 minutes on a 1700 MHz computer. The effects of the operationcan be observed with reference to the frames shown in FIGS. 2A and 2B.FIG. 2A shows an original frame of MPEG-2 video. FIG. 2B illustrates areconstruction of the frame using only the DC coefficients.

Since the video is recorded using an omni-directional camera, the imageis doughnut-shaped. As can be seen in FIG. 3, for example, the pixels(or the DC coefficients) in the outer parts of the doughnut videocontain less object information (i.e. more pixels per object), thereforetheir weight is less than the pixels (or the DC coefficients) that aretowards the inner parts. Thus, when computing the frame differences, thepixel values (or the DC coefficients) are weighted according to theirlocation to compensate for this. It can be appreciated of course thatfor a rectangular-shaped video frame, the weight values ω(r) are unity.

The weights of the pixels are assigned to compensate for this ratio whencomputing the frame pixel differences. The assignment of weights isaccomplished by considering the parabolic properties of the mirror asfollows:

$\begin{matrix}{{{\omega(r)} = {1/{\cos^{- 1}\left\lbrack \frac{1 - {4\left( {r/R_{\max}} \right)^{2}}}{1 + {4\left( {r/R_{\max}} \right)^{2}}} \right\rbrack}}},} & {{Eqn}.\mspace{14mu} 1}\end{matrix}$where r is the radius of the DC coefficient location in frame centeredpolar coordinates and R_(max) is the maximum radius of the donut image.The coefficients that do not contain any information (the location thatcorresponds to outside of the mirror area) are weighed zero.

We employ a window size of 9×9 DC coefficients, which corresponds to a72×72 pixel area, though it can be appreciated that other suitablewindow sizes corresponding to different pixel areas can be used. Theweighted luminance difference is computed for every possible location ofthis window in a video frame. A local visual activity measure (score),V_(a), can be defined as the maximum of these differences as follows:

$\begin{matrix}{{V_{a} = {\max\left\{ {\sum\limits_{{n = L},2}^{L/2}{\sum\limits_{{m = L},2}^{L/2}\left( {{\omega\left( \sqrt{\left( {x + n} \right)^{2\;} + \left( {y + m} \right)^{2}} \right)}A_{{x + n},{y + m}}} \right)}} \right\}}},{\forall_{x}{= \left\lbrack {{{- W}/2} + {{L/2}\mspace{14mu}\ldots\mspace{14mu}{W/2}} - {L/2}} \right\rbrack}}\;,{{\forall y} = \left\lbrack {{{- H}/2} + L + {2\mspace{14mu}\ldots\mspace{14mu}{H/2}} - {L/2}} \right\rbrack},} & {{Eqn}.\mspace{14mu} 2}\end{matrix}$where Wand H are the width and height of the video frame (in number ofDC blocks);

-   -   L is the size of the small local activity frame (in number of DC        blocks);    -   ω(r) is the weight of the DC block at location r (in polar        coordinates); and    -   A_(ij) is the luminance difference between two blocks at        location (i×8, j×8) in two consecutive I frames.        It is noted that Eqn. 2 is equally applicable to non-MPEG        encoded video. For example, for unencoded video, it can be        appreciated that the foregoing operations can be performed on        pixels rather than DC blocks. More generally, depending on the        specific encoding, a “unit pixel block” can be defined as being        the smallest image unit on which Eqn. 2 operates. Thus, in the        case of MPEG video, the “unit pixel block” is a DC block of 8×8        pixels. For unencoded video, however, the “unit pixel block” can        be a single pixel. A unit pixel block can be square (e.g., 2×2        pixel block, or more generally an N×N block), it can be        rectangular (e.g., M×N, M≠N), or some other shape.

FIG. 4 shows a plot 402 of computed local visual activity scores versustime for a meeting video. As shown in the figure, many of the peaks ofthe visual activity score correspond to significant visual events. Forexample, at event (a), the video frames taken at time t and (t+Δt) showa person taking his place at the table, which can be deemed to bevisually significant event. The corresponding computed visual activityscore plotted at point 402 a exhibits a large value, illustrating thecorrelation between the computed metric and the visual event. Similarly,the video frames showing a person leaving the meeting room at event (c)also corresponds to a large value at point 402 c, again indicative of avisually significant event. Events (d) and (e), showing a personentering the room also yield a large computed score, at points 402 d and402 e, respectively. In a particular embodiment of this aspect of theinvention, the video is encoded using MPEG encoding, so the video frameat (t+Δt) is the next I-frame in the MPEG stream.

On the other hand, the video frames shown for event (b) are deemed notto have visual significance, since the activity is simply the personmoving closer to the camera. Nonetheless, this segment has a largecorresponding activity value 402 b because the motion toward the cameraappears as a large moving object due to the change in perspective.Exclusion of such segments from the category of important visual eventscan be achieved if we compensate for the distance of the objects fromthe camera by utilizing techniques such as stereovision. Despite thisanomalous behavior, it can be appreciated that the foregoing localvisual activity score can nonetheless serve as the basis for determininga visual significance metric for a frame of video. As will be discussedbelow, the visual significance metric in turn can serve as a basis foridentifying visually significant events in a video recording.

Another aspect of the metric generation functionality 106 shown in FIG.1 is generation of audio significance scores from the captured audio.Our goal is to find significant segments of audio, which can becomprised of arguments and discussion activity among meetingparticipants. Though high amplitude audio resulting from a raised voiceprovides a good indication of the presence of emotion, it was discoveredthat amplitude by itself is not a sufficient indication of a significantaudio event. For example, the sound of a door slamming or the noise froma heavy object falling to the floor do not constitute significant audioevents.

In accordance with the present invention, we combine audio amplitudeinformation with sound localization information from our meetingrecorder system. It was discovered that sounds coming from differentdirections in a short time window indicates a discussion among severalspeakers potentially of a salient topic, and thus warrants attention asa significant audio event.

Thus, in accordance with an aspect of the invention, we define a speakeractivity measure, S_(a), as:

$\begin{matrix}{{{S_{a}(t)} = {\sum\limits_{n = {{- W}/2}}^{n = {W/2}}{{G(n)}{C\left( {t + n} \right)}}}},} & {{Eqn}.\mspace{14mu} 3}\end{matrix}$

-   where t is a time index (e.g., units of seconds) measured with    respect to the time frame of the meeting recording, e.g., time index    t=0 seconds might be at the beginning of the meeting recording;    -   G(n) is the smoothing filter coefficient;    -   W is the length of the smoothing filter; and    -   C(t) is the number of changes (transitions) in the sound        direction at time t.        This metric is a measure of the conversational activity among        the participants in the meeting. G(n) can be any suitable        smoothing filter. For example, in an embodiment of the invention        a Gaussian filter is employed. C(t) is either the number of        changes in the sound direction or the number of speaker changes,        measured in the time frame of [t−½, t+½]. The time index t can        be any time unit; here we use seconds. For example, if the sound        direction was constant between [t−½, t+½] seconds, C(t) takes        value “0”. If there was one sound direction or speaker change,        then C(t) takes value “1” and so on.

Audio activity measure, U_(a)(t), is defined as:

$\begin{matrix}{{{U_{a}(t)} = {{S_{a}(t)} \times {\sum\limits_{k = {{- f}/2}}^{f/2}{{X\left( {{f\; t} + k} \right)}}}}},} & {{Eqn}.\mspace{14mu} 4}\end{matrix}$where S_(a)(t) is speaker activity at time index t,f is the audiosampling frequency, and X(n) is the audio amplitude of the n^(th)sample. In accordance with this particular embodiment of the invention,the audio activity measure is the basis for the audio significancescore.

FIG. 5 illustrates experimental results of recordings processed inaccordance with the invention; in this particular example, audio clipsfrom a staff meeting recording. The figure includes a sound localizationplot 502, showing locations of sound originations recorded over time.Here, rather than the absolute values of the sound directions, thechanges in sound directions are significant. C(t), which is used tocompute S_(a)(t), is computed based on a count of changes in the sounddirection in a [t−½, t+½] window. The sound localization plotgraphically illustrates the changes in sound direction which occurduring a conversation among many people. Audio activity is representedby an audio activity plot 504 which is a plot of audio activity metricsU_(a)(t) versus time t. In the example shown, a transcript of each ofthe audio clips from the meeting discussion is provided in the dialogboxes 512, 514.

The audio activity plot 504, exhibits high regions 522 and 524, whichcorrelate with the speaker activity shown in the dialog boxes. Forexample, the dialog box 512 indicates an exchange of ideas among fourspeakers. The speaker activity is illustrated in the region 522 of thesound localization plot 502, indicating a local increase in the rate ofchange of the sound location. The speaker activity is shown by the peaksin the corresponding region of the audio activity plot. Similarly, thedialog box 514 indicates an even more animated interchange of ideas.This can be seen by inspecting the corresponding period of time on thesound localization plot 502 at region 524. The corresponding region oftime on the audio activity plot 504 shows a region of peaks in the audioactivity metrics.

It can be appreciated therefore that by analyzing various parameters ofthese audio plots, significant audio events can be identified. Forexample, it was discovered from initial experiments with severalrecordings of staff meetings, presentations, and brain stormingsessions, that the peaks in the audio activity plot 504 bear acorrespondence to the audio clips with a high degree of meetingparticipant interactions and few silent periods.

Still another aspect of the metric generation functionality 106 shown inFIG. 1 is generation of textual scores. In accordance with theinvention, the audio recording of a meeting can be transcribed toproduce a rich source of text. Language analysis techniques are commonlyused to summarize documents and audio transcriptions. Here, we computethe well-known Term Frequency-Inverse Document Frequency (TF-IDF) onmeeting transcriptions in order to find segments that contain importantkeywords. TF-IDF is defined as TF-IDF=tf/df where tf is the frequency ofa word in a document and df is the frequency of the same word in acollection of documents. For a transcript of a meeting recording, thecollection of documents comprises the collection of transcripts fromvarious meetings.

This measure is employed in our system as follows, referring to FIG. 7.A transcript 704 is produced from the audio component 702 of the meetingrecording. A TF-IDF score is produced for each word in a set of words706 taken from the transcript. The words having the highest TF-IDFscores define a set of keywords 708. For example, the keywords can bethose words 706 having TF-IDF scores which exceed some pre-selectedthreshold value. It can be appreciated that other techniques can be usedto define the set of keyword; however, TF-IDF is recognized as atechnique for providing reliable performance in many applications.

In order to find when a given keyword most frequently occurs, we dividethe audio transcriptions into audio segments 710 of some duration (e.g.,transcriptions of 10-second audio clips) and compute a documentoccurrence score DO_(k) as follows:

$\begin{matrix}{{{{DO}_{k}(i)} = {\sum\limits_{n = {{- W}/2}}^{n = {W/2}}{{G(n)}{O_{k}\left( {i + n} \right)}}}},} & {{Eqn}.\mspace{14mu} 5}\end{matrix}$where i is the audio segment number;

G(n) is the smoothing filter coefficient;

W is the length of the smoothing filter; and

O_(k)(i) is the number of occurrences of the keyword k in the audiosegment.

The audio segment with the highest DO_(k) value is defined as thekeyword audio segment 712 a for keyword k. These mapped audio segments712 can be used to enhance accessed segments (e.g., video skims) of themeeting recording. For example, video clips corresponding to the keywordaudio segments can be added to the video skim.

FIG. 6 is a high level generalized flow diagram, illustrating the basicprocessing for identifying and accessing summaries of a meetingrecording comprising significant segments from the meeting recording inaccordance with the invention. Our goal is to find meeting segments thatcommunicate the salient content of a meeting most efficiently. Theprocess begins with a step 602 of obtaining a recording of the meeting.As can be appreciated from the foregoing, a meeting recording cancomprise various modalities of recorded information, including video,audio, and textual information. In accordance with a particularimplementation of the invention, the recorded meeting information isreduced to digital form for subsequent processing. For example, videoinformation can be encoded in a suitable MPEG format, audio cansimilarly be digitized, and textual information can be converted byappropriate optical character recognition (OCR) software.

In a step 604, significance scores are computed from the capturedrecorded meeting information. For example, in an embodiment of theinvention, a visual significance score (V_(a)) can be produced for eachvideo frame of the video component of the captured recording meetingbased on local luminance metrics computed for that frame, such asdiscussed above. Similarly, an audio significance score (U_(a)) can becomputed in the manner described above, using sound localizationinformation contained in the audio component of the recording meeting.Transcripts can be generated from the audio component and processed sothat each of a number of keywords identified in the transcript can beassociated with one or more audio clips (keyword audio segments) thatare deemed to be significant with respect to that keyword.

Next, in a step 606, the computed scores generated in step 604 can beranked; e.g., sorted by magnitude. In accordance with an embodiment ofthe present invention, the visual significance scores (V_(a)) and audiosignificance scores (U_(a)) are sorted. Similarly, computed keywordaudio segments (those with the highest DO_(k) values) are sortedaccording to the TF-IDF of the keywords.

Access to the meeting recording can be provided in the form of extractedportions of the meeting recording, in a step 608, based on one or moreof these scores. In a particular implementation of the invention, theextracted content can be presented as a video skim (comprising, forexample, video and audio) that is played on a suitable display; e.g., anaudio-video capable system such as a PC with speakers. A video skim canbe produced based on a visual significance score. Alternatively, thevideo skim can be based solely on an audio significance score. Stillanother alternative is a video skim based on a combination of two ormore of these and other scores, or on a composite score computed as afunction of two or more of these and other scores.

Suppose the recorded meeting is accessed as a one minute video skimbased solely on a given visual activity score. A video frame having thatscore is selected from the recorded meeting data and can be the basisfrom which the video skim is produced. In this case, the video skimmight comprise a one minute segment of consecutive video frames (videoclip) from the recorded meeting starting with the selected video frame.Alternatively, the video clip might be selected from the recordedmeeting such that it ends with the selected video frame. A segment ofthe recorded meeting can be selected which includes the selected videoframe somewhere in the video clip. If appropriate, the video clip mighteven be a segment of the recorded meeting that precedes (or follows) theselected video frame by some amount of time. It might be desirable tocompose the one minute video skim by combining a series of shorter videoclips of equal or unequal duration. The video clip can be presented asvideo-only, absent a corresponding audio track. Such a presentationmight be desirable, depending on the purpose of the users. Of course,the video skim can include the corresponding audio track.

Depending on performance, storage capacity, and other such criteria,these video skims can be produced on a demand basis; that is, only whena visual significance score is presented. On the other hand, a videoskim for some or all of the visual significance scores can be generatedoffline and stored for later retrieval, thus allowing faster accessalbeit possibly at the expense of providing substantial storagecapacity.

Content of the recorded meeting can be accessed (e.g., a one minutevideo skim presentation) based solely on a given audio significancescore. Again, suppose a one minute video skim is desired. The time indexcorresponding to the given audio score is determined. In this case, thevideo skim can comprise a one minute segment of the video (video clip)taken from the recorded meeting based on that time index, includingpossibly the audio track. The video clip can begin or end at that timeindex, or the video clip can span a period of time that includes thetime index. The video clip might even be for a period of time that isearlier than the time index or is later than the time index. It may bedesirable to compose the one minute video skim by combining a series ofshorter length video clips of equal or unequal duration. Also, it ispossible to store video skims for some or all of the audio significancescores in an offline manner as discussed above.

It can be appreciated that as an alternative to composing and presentinga video skim, an audio-only segment corresponding to the given audiosignificance score can be presented; though such a presentation may notbe as effective as a presentation that includes the video track.However, in situations where no corresponding visual information isavailable, an audio-track only presentation would be appropriate.

The recorded meeting can be accessed by presenting a video skim that isgenerated based on a given visual significance score and a given audiosignificance score. For example, consider again that a one minute videoskim is desired. The video skim can comprise a first segment of videobased on the visual activity score as discussed above and a secondsegment of video based on the audio score as discussed above. Thesegments can be of equal or unequal duration.

It can be appreciated that where two non-consecutive video clips areplayed back, it may be desirable to “stitch” together the clips in orderto smooth out the transition between clips to minimize the distractionof a sudden scene change. For example, a wipe transition (e.g., right toleft) can be applied to the video tracks to transition from one clip tothe next. Of course, other transitions can be used. Transitioning of thecorresponding audio tracks from one video clip to the next can beachieved by audio fades or the like. If any two clips are close in timeor if they overlap, they can be merged into a single clip to achievecontinuity.

Continuing with FIG. 6, consider an example where a user might requestthe most significant video skim of the meeting (step 610) of say fiveminutes in duration. The video skim can be produced by taking thehighest valued visual significance score from the ranked list of scores(step 604) and accessing the corresponding video frame. The video skimcan then be produced from that video frame in the manner describedabove. In the case where two or more scores have the same highest value,a random selection can be made to select from among the two or morecorresponding video frames as the basis for generating the video skim.Another selection criterion might be to choose the earliest video frame,or the latest video frame, or the middle-most video frame. A request canbe presented to the user to make the selection of video frame. Stillanother resolution might be to use all the video frames combined in themanner discussed below to produce the video skim.

Further in accordance with the invention, the video skim can be producedfrom plural video frames. In this approach, the first N highest-valuedvisual significance scores can be selected from the ranked list ofvisual activity scores (step 604). A video clip can be produced for eachof the selected N video scores in the manner described above. The timeduration of each video clip can be equal or unequal. The video clips canthen be stitched together using suitable transitioning techniques toproduce a video skim of the desired duration; e.g., five minutes. Thevalue of N can be a user-specified quantity. It can be appreciated thatthe duration of each video clip comprising the video skim are dependenton the duration of the video skim and the value of N. Generally, thefollowing relation should hold true:

$\begin{matrix}{\left. D \right.\sim{\sum\limits_{i = 0}^{i = {N - 1}}d_{i}}} & {{Eqn}.\mspace{14mu} 6}\end{matrix}$where D is the duration of the requested video skim,

N is the number of video clips comprising the video skim, and

d_(i) is the duration of the i^(th) video clip.

The approximation sign (˜) reflects the possibility that the totalduration of the final video skim may be longer than (or shorter than)the duration D of the desired video skim, owing largely to theapplication of transition effects between discontinuous segments.However, due to merging of close-in-time segments, and other suchfactors, the total time might come in at less than D. It can be seenthat the parameters D and N, and each d_(i) can be user-specified, orautomatically determined.

Similarly, the most significant video skim of a recorded can be based onthe audio significance scores. The video skim can comprise the videoclip produced based on the time index corresponding to the highestvalued audio significance score in the manner discussed above. Where twoor more scores have the same highest value, a resolution can be achievedin a manner as discussed above in connection with visual significancescores.

Alternatively, the video skim can comprise a plurality of video clipsgenerated based on the audio scores. Thus, the first M highest audioscores can be selected, from which M time indices can be obtained. Thevideo skim can comprise the plurality of video clips produced based onthe time indices in the manner discussed above.

In still another variation, the most significant video skim of arecorded meeting can be composed based on visual and audio significancescores. In a simple composition, the video skim comprises a first videoclip based on the highest value visual activity score and a second videoclip based on the highest value audio score. Alternatively, the videoskim can comprise a first plurality of video clips based on the Nhighest visual activity scores and second plurality of video clips basedon the M highest audio scores.

It can be appreciated that video skims can be generated in various waysusing the visual and audio scores. It can be further appreciated thatthe next most significant video skim could comprise video clips based onthe next highest visual activity score, or on the next N highest visualactivity scores, or on the next highest audio score, or on the next Mhighest audio scores, or on both the visual and audio scores.

In accordance with another aspect of the present invention, a singlemetric can be computed by combining the video, audio, and textual scoresto produce a composite score. For example, an arithmetic combination canbe performed such as multiplication of the scores, addition of thescores, or some combination of multiplication and addition. Generally,it can be appreciated that some analytical treatment can be performed toproduce the composite score.

If the composite score is computed for a meeting moment at time t, ameeting segment can be defined as the meeting recording between [t−Δt,t+Δt]. Segments from the recording meeting corresponding to N suchscores can then be selected and combined into a video skim of themeeting recording.

The video skims can be enhanced with additional video clips. Thefollowing discussion applies to each of the above-described processesfor generating video skims. For example, consider when the mostsignificant event is to be accessed from the recorded meeting. A videoskim representing this can be produced in accordance with any one of anumber of various embodiments of the invention. As another variation,the video skim can further comprise a video clip extracted from therecorded meeting based on the keyword audio segment metric (DO_(k))having the highest value. The keyword audio segment and itscorresponding video track can be included along with the other videoclips which comprise the video skim.

From the foregoing, it can be appreciated that additional modes ofinformation can be captured and incorporated into the process foridentifying significant events. These additional modes of informationcan be captured by appropriate devices collectively represented in FIG.1 as other capture devices 104 d. For example, slide presentations canbe captured and used to identify significant meeting events. Slideprojection systems can be equipped with digitizing devices to capturethe image including text. Personal computers can include slideproduction software which can store the slide contents for subsequentprocessing. If necessary, optical character recognition (OCR) techniquescan be applied to the captured slide images to extract textual contentfrom the slides. TF-IDF analysis can be performed on the text andassociated with “events” from the slide presentation. Thus, for example,an event can be the time when the title slide is presented. Other suchevents can be the time when an outline of the presentation is presented,or when the conclusions are presented.

Events may include capture of audio information from speakers during theslide presentation. For example, when the presenter(s) introducethemselves this could be deemed a significant event. When a discussionensues among the participants, this might signify a relevant event.Another event might be the slide that includes a discussion of the maintopic. A significant event is likely to be the slide that the speakerspent the most time discussing. Likewise, a significant event might bethe slide that resulted in the most discussion as determined based onthe audio activity metric. In addition, these discussion events mightinvolve speech capture of the discussion participants and subsequenttranscription of the captured speech. The above-described analyticaltechnique can be applied to the transcribed speech text to producedocument occurrence scores DO_(k), where each keyword k is associatedwith the audio clip (keyword audio segment) having the highest DO_(k) asdescribed above. The slide(s) presented during the time period spannedby that audio clip can be associated with that keyword.

Another mode of information can be information captured from awhiteboard. Again the text can be captured and transcribed by variouswhiteboard capture devices which are known and commercially available.Discussion-related events can be detected based on the audio activityscore. Keywords from the discussions can be processed by capturing thediscussion and converting the captured speech to text. In addition toits associated audio clip (keyword audio segment), each keyword k can befurther associated with the captured whiteboard informationcorresponding to the time period spanned by that audio clip. Otherevents might include noting when a person steps up to the whiteboard touse it. Such an event can be detected based on a video activity metricgenerated from the captured video recording.

More often than not, the mainstay of a meeting is paper. The agenda of ameeting is likely to be on paper. The topics of discussion are typicallydisseminated on paper, a copy provided for each participant. Notesjotted down during the meeting are recorded on paper. This represents alarge source of useful text that can be subject to analysis,particularly notes produced during the meeting. The text contained inthese papers, can be converted to text via OCR processing. Events can bedetermined based on the note taking activity. For example, thetime-spans can be recorded of when meeting participants take notes. Animportance score can be assigned to a time-span based on the number ofpeople taking notes, length of the notes, and so on.

Identification of meeting participants can be used as a basis forenhancing detected events. For example, the video of an event determinedto be significant by any of the foregoing techniques can be furtheranalyzed to identify the participants in the video. An importance scorecan be assigned to the current speaker based on the speaker's rank inthe company. Those meeting segments containing higher ranked employeesmay potentially be more important than other segments. The importancescore can be based on speaker dominance. If one or several speakersdominate the meeting, the meeting segments containing those speakers arepotentially more important than other segments. The importance score canalso be based on the number of active speakers. The first or the lasttime a speaker speaks can be potentially more important than the rest ofthe meeting, and so the importance score can include that consideration.

Thus, in accordance with another implementation of the invention, thevarious modalities (video, audio, various forms of text) can be scoredand used to identify significant meeting segments. For example, animportance score can be computed for each modality associated with eachsecond of a meeting. The importance scores can then be combined toproduce a significance score, for example by multiplying together all ofthe scores, or by summing all the scores, or by some other combination.The significance scores can then be ranked.

A video skim might comprise a number of 10-second segments of themeeting recording. For example, the n^(th) most significant video skimof a meeting can be defined as a group of M segments, where the segmentsare based on the n^(th) group of M consecutive significance scores takenfrom the ranked list. Thus, the meeting segments comprising the mostsignificant video skim can be selected by taking 10-second segments ofthe meeting recording at those time indices having the M highestcorresponding significance scores.

It was noted earlier that an embodiment of a meeting recorder inaccordance with the present invention can be a portable device. Aportable meeting recorder offers certain advantages, but presents itsown unique challenges. Unlike a meeting recorder which might be based oninstrumentation of a conference room where most meetings are carried outin one place, meetings recorded with a portable meeting recorder cantake place in different locations. Identifying the meeting location canprovide a very useful retrieval cue to facilitate identifyingsignificant segments of a recording of a meeting that comprises manymeetings taking place in various locations. One possible solution is toincorporate a GPS device into the portable meeting recorder. However,the accuracy of current GPS technology may not be sufficient toaccurately identify the meeting locations, especially considering thatthey take place indoors.

Referring to FIG. 8A, our solution is based on recognizing a meetingroom (or more generally a meeting location) from visual clues containedthe video data components of a meeting recording. We first performbackground/foreground extraction as the recorder is manually operatedand therefore it is unreasonable to assume that a clean shot of thebackground can be obtained with no person in the room. We use adaptivebackground modeling to extract the background. Our algorithm is based onan extension of a method discussed by Stauffer, C. and Grimson, W. E. L,Adaptive Background Mixture Models for Real-Time Tracking, Proceedingsof Computer Vision and Pattern Recognition, pp. 246-252, 1999. AGaussian mixture approximates the distribution of values at every pixelover time. For each Gaussian constituent, its likelihood of beingbackground is estimated based on its variance, frequency of occurrence,color and neighborhood constraints. From this, an image of thebackground can be constructed based on the most likely backgroundGaussian at every pixel. Since this background estimate changes overtime, for example due to the movement of objects in the room, we extracta new image every time a significant change in the background model isdetected. These images are dewarped into a panoramic cylindricalprojection as shown in FIG. 8A. The first image in the figure shows themeeting room with some background and foreground objects. The secondimage shows the foreground. The third image shows the backgroundseparated by our algorithm.

To identify the location, the background images can be matched againstroom templates in the database. Since the number of placements for therecorder in a particular room is usually limited, the templates can becategorically organized and stored as separate templates. In our case,one template is obtained from each end of a table in a conference room.We match the templates with the backgrounds of the meeting recordings bycomparing their color histograms. The histograms are formed in the HSVcolor space because distance values in this space approximate humanperception. The color space represented with 256 bins, where Hue isquantized into 16 bins, and Saturation and Value are quantized into 4bins each.

Several background images are extracted for each meeting and anintersection histogram is computed using the histograms of these images.The intersection histogram is compared using Euclidian distance witheach template in the database to find the closest matching meeting room.Employing an intersection histogram allows us to further eliminate thenon-stationary objects in the meeting room and smooth out any backgroundextraction errors. The use of multiple templates for each room providesa robust method for location identification. In our experiments, wesuccessfully identified the 5 meeting rooms that we have in our researchfacility. Improvements to the algorithm might include using the size andthe layout of the meeting room to address the issue of distinguishingrooms with similar colors.

Recorded meeting segments can be enhanced by further identifying andlocating the meeting participants in the recorded segment. Locatingmeeting participants is a non-trivial problem, especially consideringthat a clean shot of the background is typically not available andparticipants are likely to have minimal motion. We address this problemby using sound localization to find the approximate location of eachmeeting participant. Then the precise location of each face is found byidentifying the skin regions in this approximate location.

Skin pixels are detected in the normalized RG-space as discussed, forexample, by Waibel, A., Bett, M., Metze, F., Ries, K., Schaaf, T.,Schultz, T., Soltau, H., Yu, H., and Zechner, K., Advances in AutomaticMeeting Record Creation and Access, Proceedings of the InternationalConference on Acoustics, Speech, and Signal Processing, 597-600, 2001.Small holes in skin-colored regions can be removed by a morphologicalclosing and then connected component analysis can be used to identifyface region candidates. In environments with complex backgrounds, manyobjects, such as wood, clothes, and walls, may have colors similar toskin. Therefore, further analysis of the skin-colored regions, usingtechniques such as luminance variation and geometric feature analysiscan be applied to further eliminate non-face regions. Some example facelocalization results are shown in FIG. 8B.

One of our goals is to find representative shots of the meetingattendees that can be included in the meeting description document. Itis possible to extract many shots of a participant from the videosequence. However, generally not all of these shots are presentable. Itis desirable to obtain frames where the individual is not occluded andfacing the camera.

An example of obtaining a “best shot” from a video sequence is shown inFIG. 9. First, the source video (in this case, the doughnut shaped video902 from an omni-directional camera) is captured. Several still shots ofthe speaker are then extracted. In one implementation, soundlocalization information 912 is used to identify candidate still shots;e.g. a still from when she/he first starts speaking 904 a, a still fromwhen she/he finishes speaking (for the first time) 904 b, and onebetween these two times 904 c.

These shots are then evaluated to pick the best shot of a meetingparticipant. For example, the best shot 910 can be selected byevaluating the size of the face region relative to the size of the bodyregion and/or the whole frame, evaluating the ratio of the face widthand height, and evaluating the ratio of number of skin pixels detectedin the best-fitted ellipse (to the face region) to the area of thebest-fitted ellipse. FIG. 9 shows various regions 906 that can beconsidered. The larger faces with more skin pixels are selected asbetter shots.

In a specific implementation, high resolution video capture devices canbe used to produce high resolution images of the participants. Thisprovides sufficient resolution in the captured video for the applicationof computer image analytical techniques to identify certain facialfeatures, such as eye and mouth regions. The selection of the bestattendee shots can be based on the detail of the facial features, suchas the mouth and eyes, the distances between these facial features andtheir relative locations, the size and geometry of the face region, andso on. When the image of the face of a person looking straight at thecamera is taken, these parameters tend to fall within certain ranges.These ranges can be the basis for computing templates to facilitatemaking a determination whether a person is looking straight at thecamera.

FIG. 9A shows a schematic representation of a template, highlightingsome possible facial metrics that can be used to create a template. Forexample, the template 902 comprises a pair of eye regions, a noseregion, and a mouth region. Some metric exemplars of the template mightinclude an eye separation D_(p), measured between the center of eacheye. A nose angle θ_(n) can be measured as the angle between two linespassing through an interior of the nose region to the center of eacheye. A mouth separation D_(m) can be the distance from the eye to themouth. Similarly, a nose separation D_(n) can be the distance from theeye to the nose.

In a face image of a meeting participant who is facing away from thecamera, the various facial features metrics will deviate from thetemplate metrics. For example, in a face image of a person whose face isturned to one side, the eye separation will be less than if the personhad faced the camera squarely due the effect of the image of the eyesbeing projected onto a two dimensional surface. Thus, a best shotdetermination from among many “shots” (images) of a meetingparticipant's face can be made, first by identifying the facial featuresof interest for a given image. Next, metrics among the facial featuresare determined, such as shown in FIG. 9A. These determined metrics arethen compared against their corresponding template metrics. A errorcomputation can be performed to produce an error quantity for thatimage. For example, each template metric can have an associatedtolerance to establish a tolerance range. Each determined metric can becompared against the corresponding template metric to determine it fallswithin or outside of the tolerance range. If the determined metric fallsoutside of the range, the amount of deviation can be recorded. Acomposite error quantity can be computed based on the number ofdetermined metrics that fall within the tolerance range and the amountof deviation of the determined metrics that fall outside of thetolerance range. The process is repeated, and the image having thesmallest error quantity can be deemed to be the best shot of thatperson.

The locations of the eye pupils can be detected using an IR emitterattached to the meeting recorder as an enhancement to the camera. (Forexample, J. Davis and S. Vaks in A Perceptual User Interface forRecognizing Head Gesture Acknowledgements, ACM Workshop on PerceptualUser Interfaces, Orlando, Fla., Nov. 15-16, 2001, describe such asystem.) Sound localization can then be combined with the IR-based eyepupil detection to eliminate false positives in pupil detection. Recallthat sound localization data includes at each time index informationindicating the direction of each sound source. The images each can beassociated with a time index. Each image that is analyzed for pupilidentification can be cross-referenced with the sound localization databased on the time index associated with the image in order to provide anadditional decision point for pupil identification.

For example, suppose an image is being analyzed (using known computerimage analysis algorithms) to identify pupils. Suppose further that apositive determination has been made, indicating the presence of pupils.On the one hand, one can simply assume the determination to be correctand continue on. On the other hand, the sound localization data can beused as an additional test to add some confidence to a positivedetermination. The time index associated with the image is used toaccess the sound localization data corresponding to that time index. Ifthe sound localization data does not indicate that any sound originatedfrom the direction of the camera at that time index, then such anindication can be interpreted to mean that the positive determinationwas a false positive, and the quest for eyeballs would continue. On theother hand, if the sound localization data indicated a sound originatedfrom the direction of the camera at that time index, then the positivedetermination can be deemed to have been confirmed. Generally, employingthe additional information of the IR data and the sound localizationdata can increase the reliability of identifying facial features, inaddition to identifying pupils. Furthermore, it can be appreciated thatan analogous process can also be used to reinforce the confidence of thesound localization results based on the pupil location data.

FIG. 9B is a schematic representation of an image 922 taken by a camera,illustrating the metrics in an alternative embodiment for determiningbest shot. Here, pupil location/orientation and facial orientation canbe used to make the determination. The pupils can be identified in animage in the various manners discussed above. In addition, a headoutline 934 is also identified from the image. After the pupils aredetected, their location/orientation can be determined. In thisembodiment, pupil orientation is made relative to a frame of referenceR; for example, the edge of the image. Pupil orientation can be definedas an angle α of a pupil line 912 passing through the identified pupils932 relative to an edge (e.g., 922 a) of the image, although any edgecan be used, or any other constant point of reference such an alignmentmark 924 that can be superimposed on every image at a constant location.Pupil location D can be based on the location of the pupil(s) 932relative to the detected outline of the head 934. The orientation of theface (head) can be based on a line of symmetry 914 vertically bisectingthe detected outline of the face relative to a reference; e.g. the edgeof the image. Here, orientation is shown as an angular measurement β. Itcan be appreciated that the orientation can be defined by other similarmetrics.

Pupil location metrics can be used to indicate the degree to which aperson is facing directly at the camera and thus serve as a basis fordetermining the best shot. In an image of a person squarely facing thecamera, the pupils 932 will be generally symmetric with respect to theface outline 934. For example, metrics such as pupil distances from theleft and right side of the face outline typically will be symmetric.Thus, for example, the D₁ and D₃ measurements should be close, and theD₂ and D₄ measurements should be close. It can be appreciated that othersuch symmetry measurements can be used. When the head is turned, thesemeasurements would no longer be symmetric due to the effect ofprojecting a three dimensional object onto a two dimensional surface.

Pupil and face (head) orientation can then be used in conjunction withpupil location to further facilitate determining best shot selection inthe following manner. These metrics can indicate the degree of rotationof the head. For example, a person who has fallen asleep is not likelyto be facing the camera directly, but rather will be facing the table.Thus, for the metrics shown in FIG. 9B, the vertical bisector 914 willhave a β of about 90°, as will the pupil line 912. Thus, each candidateimage of a person being considered for best shot selection will beanalyzed to determine the value of these metrics. The candidate imagehaving the most symmetry in terms of pupil location and having α and βangles that show the least total deviation from 90° will be selected asthe best shot image for that person.

FIG. 10 shows an illustrative example of a user interface in accordancewith the invention. Meeting recording segments and computed scores canbe presented to the user via a suitable GUI as shown in the figure. Inthis particular embodiment, the GUI is a Java application that supportsvideo editing, and video playback control via control buttons 1002. In aparticular embodiment of the invention, enhancements are provided to aninterface that is more fully disclosed in U.S. patent application Ser.No. 10/081,129 and in U.S. patent application Ser. No. 10/174,522. Theinterface is a GUI referred to as the Muvie Client.

The Muvie Client can be enhanced with additional navigational toolsbased on key frames 1012 and the transcript 1014. A slider windowgraphic 1016 can be provided which allows the user to scan up and downscales representing speaker transition activity, visual activity, andaudio activity. As the slider is manipulated, a transcript window 1018containing a transcription of the relevant conversation is displayed. Asecond slider window 1020 can be provided to allow the user to selectfrom among the key frames.

Capability can also be provided for viewing slides, whiteboard images,meeting notes, both the perspective and panoramic meeting video, andaudio tracks for speaker location, as well as visual significancemeasures and audio significance measures. A timeline 1004 is avertically oriented graphic along which different channels ofinformation can be displayed. The example shown in the figure shows thetranscript, video key frames, and the results of speaker transitionanalysis, video activity analysis as well as audio activity analysis. Ingeneral, the Muvie Client enhanced in accordance with this aspect of thepresent invention, provides a tool for displaying the results of mediaanalysis of the meeting recording. For example, analytical techniquesfor video such as those embodied in accordance with the invention, andincluding similar conventional techniques can be visually represented bythe Muvie Client. Similarly, analytical techniques according to thepresent invention for audio and similar conventional techniques can bevisualized by the Muvie Client in accordance with this aspect of theinvention. Likewise for textual significance scores.

A visual representation of the speaker transitions channel is shown in1004 in which each speaker can be marked with a unique color (identifiedin FIG. 11, for example, as s1-s4). This gives the user an indication of“who spoke and when”, at a quick glance. We can determine who isspeaking from the sound localization data. This information can beenhanced with meeting participant tracking to improve the accuracy ofspeaker identification as each participant moves about in the meetingarea. The output of this process can be provided to the Muvie Client inthe following XML format:

<MUVIE-SPEAKER> <SECTION> <TIME>0000</TIME> <LOCATIONS> <SPEAKER><LOC>1</LOC> <VALUE>1</VALUE> <ID>255</ID> </SPEAKER> <SPEAKER><LOC>2</LOC> <VALUE>0</VALUE> <ID>0xD100FE </ID> </SPEAKER> . . .</LOCATIONS> </SECTION> <SECTION> . . . </SECTION> . . .</MUVIE-SPEAKER>

A SECTION tag typically marks a period of time (e.g., a one secondperiod). For each SECTION we can have more than one SPEAKER. EachSPEAKER contains the values shown. All the data for a SECTION are shownat the specified TIME on the time line. The LOC tag determines theposition of the speaker marking on the display; typically “1” is theleftmost location in the display bar. VALUE can be “0” or “1” indicatingwhether the person is speaking or not. Alternatively, VALUE can be areal number indicative of the strength of the measure of activity. TheID tag represents a particular speaker and can be used to indicate acolor value associated with that speaker. However, we typically assigncolors to speakers automatically since this gives the user some controlover the visualization.

The Muvie client parses the XML format described above and creates aspeaker data structure that can be used to create the display shown inFIG. 10. The speaker data structure contains a linked list of <SECTION>objects. Each <SECTION> object contains a <TIME> and a linked list of<SPEAKER> objects. Each <SPEAKER> object contains a <LOC> (location), a<VALUE> and an <ID>. As noted above, the <VALUE> can either be binary(“0” or “1”) or a real number indicating “strength” approximation, if amore accurate representation is desired. In the XML fragment shown,values are in binary format. When the XML file is parsed, it isdetermined how many <SPEAKER> locations to allocate for this video.

FIG. 11 is an enlarged portion of the timeline 1004, showing a graphicalrepresentation of a speaker transition channel positioned next to a textthumbnail and keyframe thumbnail graphic. The speaker transition channelgraphic is vertically arranged. The speaker transition channel shows 4subchannels s1-s4, each representing one speaker location in themeeting. A fifth subchannel s5 is shown, but no activity is indicated,suggesting that the speaker has not spoken during the segment of thetimeline shown in FIG. 11. Using the speaker data structure, the speakertransitions can be mapped into appropriate grid locations. For instance,it can be seen that the s1 speaker starts the meeting followed by the s3speaker. To map the data on the user interface, we convert the timeassociated with the <SECTION> object into a pixel location using thefollowing parameters:

-   -   height=height of current timeline (in pixels)    -   duration=duration (in seconds) of current video    -   tpix_m=height/duration (multiplier used to convert seconds into        pixels on the timeline)    -   tsec_m=duration/height (multiplier used to convert pixels into        seconds)        For example, suppose we have:    -   height=786 pixels,    -   duration=1800 seconds (½ hour)    -   tpix_m=0.4367 (pixel per second) and    -   tsec_m=2.29 (seconds per pixel)        Then, when we have a <SECTION> object with a <TIME> stamp of say        356 seconds, we can plot the associated <SPEAKER> structures at        location (<TIME>*tpix_m) or 356*0.4367=155 pixels vertically        relative to a reference location. FIG. 12 shows a result of        iterating through all of the <SECTION> objects and plotting all        of the <SPEAKER> data. In this case, each vertical subchannel in        the speaker content channel can be assigned a unique color to        distinguish it from the other subchannels.

A slider graphic 1016 (FIG. 10) can be manipulated along the timeline1004 to specify a time index. The location of the slider graphic can bemapped to one or more time indices as shown above. Using the timeindex(ices), a corresponding video skim can be produced in the mannerdiscussed above and presented in a presentation window 1022.

A representation for the visual activity channel can be graphicallyrepresented as a histogram along the time line 1004. It displays thevisual activity scores for each unit of time (e.g., seconds, minutes) ofa video sequence. The score is computed per Eqn. 2 and can be normalizedfor the display area. A slider window 1020 can be used to navigate thevideo based on the visual activity scores. The video and the rest of themetadata are automatically synchronized during the playback. The outputof visual activity analysis is given to the Muvie Client in thefollowing XML format:

<MUVIE-SECTIONS> <SECTION> <STIME>0</STIME> <ETIME>0</ETIME><VALUE>20</VALUE> </SECTION> <SECTION> <STIME>1</STIME> <ETIME>1</ETIME><VALUE>11</VALUE> </SECTION> <SECTION> . . . </MUVIE-SECTIONS>

The SECTION tag typically marks a one second period. However, it can beextended to longer periods of time. The STIME tag determines the starttime and the ETIME tag determines the end time when a particularactivity score is valid.

The Muvie client parses the XML format described above and creates adata structure that can be used to create a representation for visualactivity analysis shown in FIG. 10. The XML file contains a collectionof <SECTION> objects where each <SECTION> contains a <STIME> (starttime), <ETIME> (end time), and a <VALUE>. The <STIME> and <ETIME> valuesare represented in seconds but could also be represented inmilliseconds. At present we use the same start and stop time, but onecould easily modify this to show <SECTION> tags which depicted timeranges, e.g. 4 second time periods. The VALUE represents the visualactivity taking place at a given time during a video.

To plot this information on a user interface, as we did with the speakertransition information, a video channel is included in the timeline 1004where we plot the visual activity as shown in FIG. 13. The histogramshown in FIG. 13 is plotted with the X-axis oriented vertically and theY-axis oriented horizontally such that histogram bars with higher Yvalues represent more activity than those with lower Y values. Each<SECTION> tag contains a start and stop time. As noted above, we areusing the same start and stop time, but one could easily modify this toshow <SECTION> tags which depicted time ranges, e.g. 4 second timeperiods.

To map the data onto the user interface, we first determine the highestvalue represented in the data collection. We call this value max. Nextwe determine the width of the user interface which we call graphWidth(see FIG. 13). This value is predetermined by a default setting or byuser configuration. We use the combination of the max value and thegraphWidth to plot the values on the Y-axis. We use the tpix_m to plotthe values along the X-axis since these values are based on time.

For example, if we have a <SECTION> object with a <TIME> value of 491and a <VALUE> of 56, where graphWidth=50 and max=78, then, we have a newmultiplier graph_m=max/graphWidth or 1.56. We divide the <VALUE> bygraph_m to produce the width of the histogram bar for this <SECTION>object. Therefore, in the above example, we would plot the <SECTION>with <TIME>=491 and <VALUE>=56 at the vertical location (time)491*.4367=214 with a width of 56/1.56=35. This process is conducted oneach <SECTION> object to produce a timeline channel similar to the oneshown in FIG. 14.

A representation for the audio activity channel is shown in 1004 thatdisplays the audio activity scores for each minute (can be in seconds)of a video sequence. This value is computed according to Eqn. 4 andscaled for the display area. It is displayed, navigated, and formattedin the same way as the visual activity data described above.

Completing the description of to FIG. 10, the presentation window 1022can be utilized to display additional modalities such as captured slideimages, captured whiteboard images, and captured meeting notes. As shownin the figure, the presentation window can display perspective andpanoramic views of the meeting recording. The foregoing techniques forobtaining meeting location and meeting participants can be incorporatedin a meeting summary window 1024. Best shot selection can be used toprovide the best image available of each participant.

Using this interface, the user can browse a meeting by reading thedescription page, listening only to the speakers that he is interestedin, looking at the high-motion parts, searching for keywords in thetranscription, looking at the presentation slides and whiteboard images,and so on. In this way, hours of meetings can be browsed in much lesstime. The user interface can also support editing of the video, whichenables the user to efficiently communicate meeting documents withothers.

FIG. 15 shows an implementation of another aspect of the presentinvention in which summaries can be represented. A summary create checkbox 1502, or other such graphic, can be presented to a user. The summarycreate check box when activated (by “clicking” on it, for example) candetermine summary segments from the meeting recording and provide avisual representation. In the example shown in FIG. 15, summary windows1504 highlight those portions of the timeline which correspond to theidentified summary segments.

The meeting summaries can be generated based on individual orcombinations of visual scores, audio scores, text importance scores, andimportance score computed from slide presentations, whiteboard capturedata, and notes in the manner discussed above. The user can specifywhich modalities to use when generating the summary. Using the timeindices corresponding to the scores of the selected modality(ies),summary windows 1504 can be generated and displayed.

For example, the user can specify a one-minute long meeting summarybased on audio and visual activity importance. Thus, the N highestvisual scores and the M highest audio scores can be deemed to constitutea summary of the meeting. As discussed above, video skims correspondingto the time indices of these scores can be generated. However, ratherthan displaying video skims, the range of time indices corresponding tothe generated video skims can be used to generate the summary windows1504 shown in FIG. 15. This allows the user to browse the recordedmeeting with the summary windows acting as annotations signifying thesignificant portions of the event (e.g., a meeting) that was recorded.The highlighted sections thus can serve as a guide for meeting reviews,navigation, editing, sharing, printing, and generally making life easierfor the reviewer.

In another example, the user may request a five-minute meeting summarybased on slide importance. The slides that the presenter had spent themost time on might be considered important. Thus, the meeting summarymight comprise one-minute meeting segments corresponding to five suchslides. Again, the range of time indices corresponding to thepresentation of those slides would be used to generate summary windows1504.

As an alternative to displaying summary window graphics 1504, portionsof text in the transcription window 1514 corresponding to the timeindices can be highlighted. Similarly, corresponding keyframes 1512 canbe highlighted. It can be appreciated that other similar forms ofindicating the recorded meeting segments comprising the meeting summaryare possible.

Selection of a summary window 1504 such as by clicking can cause acorresponding video skim to be played. Similarly, clicking on ahighlighted portion of text or a highlighted keyframe can cause thecorresponding video skim to be played. Alternatively, all the videoskims comprising the meeting summary can be played sequentially byclicking on a “playback” button 1522. Navigation control through a videoskim can be provided by “reverse” and “fast forward” buttons 1524, 1526provided via the interface, allowing the user to watch only thehighlighted parts, or skip to other sections of the meeting.

FIG. 16 illustrates another GUT exemplar. Here, a markup language knownas SMIL (Synchronized Multimedia Integration Language) is a publicdomain standard that is well-suited for displaying the variety of mediathat can be present in a meeting recording. A SMIL-based interface canbe driven by the Real player display engine from RealNetworks, Inc. Theomni-directional image of a meeting recording can be dewarped asdescribed above and presented to the Real player via a SMIL script andpresented in a window 1632 in the Real player. Key frames 1634 extractedfrom the video can be shown with their corresponding time indices (timestamps) by the Real player. Clicking on a key frame can result in theaction of repositioning the video to start a playback sequence startingfrom the corresponding time index.

The interface shown in FIG. 16 can provide a meeting summary function asdescribed in FIG. 15. A summary create graphic 1602 can serve toinitiate computation of a meeting summary. Keyframes 1604 whichcorrespond to the meeting segments deemed to constitute the meetingsummary (as determined based on one or more user specified modalities)can be highlighted to indicate the location of the meeting summarysegments. Clicking on a highlighted keyframe can reposition the video inthe playback window 1632 to the time index corresponding to the“clicked” keyframe. Navigation buttons, such as playback 1622, rewind1624, and fast-forward 1626, can be provided, in addition to otherconventional navigation controls, to facilitate navigating the summarysegments.

1. A method for retrieving content comprising multimedia information,the method comprising: receiving a request to retrieve a portion of saidmultimedia information; in response to said request, producing a firstportion of said multimedia information and presenting said first portionof said multimedia information; producing a term-frequency inversedocument frequency (TF-IDF) score for each word in a plurality wordscontained in a transcript of said multimedia information; producing aplurality of keywords by taking words from said plurality of words basedon said TF-IDF scores; and for each keyword, producing a documentoccurrence score for a plurality of segments of said transcript andassociating a segment having a highest document occurrence score withsaid each keyword, said segments corresponding to portions of saidmultimedia information, wherein producing said first portion includesincorporating portions of said multimedia information corresponding toone or more of said segments of said transcript, said producing a firstportion of said multimedia information comprising: selecting a frame ofvideo from a plurality of video frames, said selecting based on a visualsignificance score, said visual significance score indicating aresponsiveness of said selected frame to said request; selecting asegment of audio of said multimedia information, said selecting based onan audio significance score, said audio significance score indicating aresponsiveness of said selected segment of audio to said request, saidsegment of audio having a corresponding video clip; generating a videoskim from said selected frame of video and said video clip; anddisplaying said generated video skim.
 2. The method of claim 1 whereinsaid transcript further comprises textual material from one or moreadditional sources comprising whiteboard input, slide presentationmaterial, and transcribed notes.
 3. The method of claim 1 wherein saidvideo information is MPEG encoded video data, and said video framescomprises I-frames.
 4. The method of claim 1 wherein said audiosignificance score is based on a measure of speaking activity.
 5. Themethod of claim 1 further comprising: determining a best shot image fromamong candidate images of a person's face comprising, for each candidateimage, steps of: identifying a plurality of facial features; determininga plurality of measurements among said facial features; and comparingsaid measurements against template measurements to produce an errorquantity, said template measurements representing a plurality of averagemeasurements of facial features, wherein a candidate image having aminimal error quantity is selected as a best shot image, wherein one ormore such best shot images of people can be displayed along with saidvideo skim.
 6. The method of claim 5 wherein said identifying aplurality of facial features includes: determining a time indexassociated with said candidate image; determining sound directionsoccurring during a period of time corresponding to said associated timeindex; identifying pupils from said candidate image; and if a positivedetermination of pupil identification occurs, confirming said positivedetermination based on said sound directions.
 7. The method of claim 1further comprising: determining a best shot image from among candidateimages of a person's face comprising, for each candidate image, stepsof: identifying pupils; identifying a head outline; determining locationmetrics of the pupils relative to said head outline; determining pupilorientation relative to a reference; and determining head orientationrelative to a reference, wherein a best shot is selected from among saidcandidate image based on said location metrics, said pupil orientation,and said head orientation, wherein one or more of such best shot can bedisplayed with said video skim.
 8. The method of claim 7 wherein saididentifying pupils includes: determining a time index associated withsaid candidate image; determining sound directions occurring during aperiod of time corresponding to said associated time index; identifyingpupils from said candidate image; and if a positive determination ofpupil identification occurs, confirming said positive determinationbased on said sound directions.
 9. A system for accessing content frommultimedia data, the system comprising: a data component configured toprovide said multimedia data; a scoring component operative to producescores corresponding to segments of said multimedia data, wherein saidscores include visual significance scores and audio significance scores;and a selection component operative to select segments of saidmultimedia data based on their corresponding scores, wherein saidselection is performed in response to a request for a portion of saidmultimedia data, wherein each of said scores indicates a responsivenessof the corresponding segment to the request, wherein said scoringcomponent is further operative to produce a video skim incorporatingportions of said multimedia data corresponding to one or more of saidselected segments, wherein said scoring component comprises a computerprogram configured to perform operations comprising: producing aterm-frequency inverse document frequency (TF-IDF) score for each wordin a plurality words contained in a transcript of said multimedia data;producing a plurality of keywords by taking words from said plurality ofwords based on said TF-IDF scores; and for each keyword, producing adocument occurrence score for a plurality of segments of said transcriptand associating a segment having a highest document occurrence scorewith said each keyword, said segments corresponding to portions of saidmultimedia data.
 10. The system of claim 9 wherein said scoringcomponent comprises a computer program configured to perform operationscomprising: obtaining a first video frame from video data contained insaid multimedia data, said first video frame comprising a plurality offirst subframes; obtaining a second video frame from said video data,said second video frame comprising a plurality of second subframes, eachof said first subframes associated with one of said second subframes;for each of said first subframes computing a local luminance metric as afunction of said first subframe and its associated second subframe,thereby generating a plurality of local luminance metrics associatedwith said first video frame; and associating a visual score with saidfirst video frame, said visual score based on one or more of said localluminance metrics, wherein said foregoing steps are repeated for aplurality of video frames in said video data to produce a plurality ofvisual scores, each of said visual scores associated with each of saidvideo frames.
 11. The system of claim 10 wherein said local luminancemetric is determined for successive I-frames.
 12. The system of claim 9wherein said multimedia data comprises MPEG (motion picture expertsgroup) video.
 13. The system of claim 12 wherein the video isomni-directional video.
 14. The system of claim 9 wherein saidmultimedia data comprises video compressed using a block-basedcompression format including MPEG-1, MPEG-2, MPEG-4, H.263, H.263L, andJVT/H.26L.
 15. The apparatus of claim 9 further including a displaycomponent operable to display said segments of said multimedia data,said display component including a computer program for determining abest shot image from among a plurality of images of a person's face,said computer program configured to perform operations comprising, foreach image: identifying a plurality of facial features; determining aplurality of measurements among said facial features; and comparing saidmeasurements against template measurements to produce an error quantity,said template measurements representing a plurality of averagemeasurements of facial features, wherein an image having a minimal errorquantity is selected as a best shot image.
 16. The apparatus of claim 15wherein said identifying a plurality of facial features includes:determining a time index associated with said candidate image;determining sound directions occurring during a period of timecorresponding to said associated time index; identifying pupils fromsaid candidate image; and if a positive determination of pupilidentification occurs, confirming said positive determination based onsaid sound directions.
 17. The apparatus of claim 9 further including adisplay component operable to display said segments of said multimediadata, said display component including a computer program fordetermining a best shot image from among a plurality of images of aperson's face, said computer program configured to perform operationscomprising, for each image: identifying pupils and a head outline;determining location metrics of said pupils relative to said headoutline; determining pupil orientation relative to a reference; anddetermining head orientation relative to a reference, wherein a bestshot is selected from among said images based on said location metrics,said pupil orientation, and said head orientation.